Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

OBJECTIVE To analyze pathogens of multiple drug-resistant organism(MDRO)infection and its risk factors during the recovery period of patients with traumatic brain injury,and to construct and validate the predic-tion model for MDRO lung infection.METHODS Patients who were admitted to the Shanxi Second People's Hos-pital for traumatic brain injury(TBI)and developed hospital-acquired pneumonia(HAP)during their hospitaliza-tion between Aug.2019 and Aug.2023 were selected as the study subjects,their clinical data were retrospectively collected,and the patients were randomly divided into a modeling group(n=270)and a verification group(n=90)at a ratio of 3∶1,and they were further categorized into the MDRO infection group(148 cases)and the non-infection group(122 cases)based on whether they developed pneumonia caused by MDRO infections.Clinical data of the patients were collected for analysis,with pathogen identification performed for all patients.The risk factors for MDRO infection in TBI patients with HAP were analyzed by multivariate Logistic regression analysis,a pre-diction model for MDRO infections during the hospitalization and recovery period of TBI patients with HAP was constructed using a formula-based method,the goodness-of-fit of the model was assessed using Hosmer-Leme-show test,and its predictive ability was evaluated using receiver operating characteristic(ROC)curves.RESULTS There was no significant differences in the baseline characteristics of TBI patients with HAP between the modeling group and the verification group.The incidence of MDRO infections among TBI patients with HAP in modeling group was 54.81％(148/270),with Klebsiella pneumoniae(25.27％)being the predominant pathogen.Mechan-ical ventilation duration(OR=5.789,95％CI:2.164-15.486,P=0.001),ICU length of stay(OR=5.441,95％CI:2.153-13.751,P＜0.001),combined kidney diseases(OR=2.770,95％CI:1.321-5.812,P=0.007),duration of antibiotics use(OR=7.486,95％CI:1.928-29.059,P=0.004),occurrence of impaired consciousness(OR=5.720,95％CI:2.586-12.652,P＜0.001)and use of aminoglycoside antibiotics(OR=3.861,95％CI:1.608-9.273,P=0.003)were risk factors for MDRO infections.The Hosmer-Lemeshow for the risk prediction model showed that x2=5.013 and P=0.496.ROC curves analysis showed The area under the curve(AUC)for the modeling group by ROC curve analysis was 0.80(95％CI:0.773-0.877),with a sensitivi-ty of 83.33％and a specificity of 73.60％respectively,and the AUC for the verification group were 0.800(95％CI:0.670-0.895),with a sensitivity of 81.82％and a specificity of 77.27％respectively.CONCLUSION The prediction model constructed in this study,based on the risk factors for MDRO infection pneumonia during the re-covery period in hospitalized patients with traumatic brain injury,demonstrated high goodness-of-fit,as well as satisfactory sensitivity and specificity,and its application in clinical practice may help identify patients at high risk of MDRO infections.

Objective To construct a multivariable prediction model for assessing the risk of cardiopulmonary complication after thoracoscopic lobectomy in patients with stage Ⅰ-Ⅱ non-small cell lung cancer(NSCLC).Methods Clinical data of 600 patients with stage Ⅰ-Ⅱ NSCLC who underwent thoracoscopic lobectomy were retrospectively analyzed.Patients were divided into the complication group(84 cases)and the non-complication group(516 cases)based on the occurrence of postoperative complication within 7 days,including atelectasis,pulmonary embolism,respiratory failure,chylothorax,massive pleural effusion,hypoxemia and atrial fibrillation.Demographic characteristics,preoperative pulmonary function assessment,pathological features and perioperative indicators were collected.Multivariate Logistic regression analysis was used to identify independent risk factors influencing postoperative cardiopulmonary complication in patients and construct a predictive model.Internal validation was performed using the Bootstrap resampling method(1 000 iterations)to evaluate the discrimination,calibration and clinical decision-making value of the model.Results Multivariate Logistic regression analysis identified the following independent risk factors of postoperative cardiopulmonary complication,including age(OR=1.832,95%CI:1.537-2.183),history of chronic obstructive pulmonary disease(COPD)(OR=6.782,95%CI:2.685-17.130),Karnofsky performance status(KPS)score(OR=0.926,95%CI:0.888-0.965),the percentage of forced expiratory volume in the first second to the predictive value(FEV1%pred)(OR=0.906,95%CI:0.845-0.972),the percentage of diffusing capacity for carbon monoxide to the expected value(DLCO%pred)(OR=0.901,95%CI:0.832-0.975),intraoperative blood loss(OR=1.025,95%CI:1.014-1.036)and one-lung ventilation time(OR=1.057,95%CI:1.034-1.080).The area under the curve(AUC)of the combined diagnosis was 0.977(95%CI:0.965-0.989),with 96.4%sensitivity and 87.6%specificity.The Hosmer-Lemeshow test indicated excellent calibration(χ2=1.285,P=0.994).Decision curve analysis demonstrated significant clinical net benefit when the risk threshold probability ranged between 20%and 98%.Conclusion The multivariable prediction model for cardiopulmonary complication after thoracoscopic lobectomy in stage Ⅰ-Ⅱ NSCLC patients exhibits strong predictive performance.

OBJECTIVE To analyze pathogens of multiple drug-resistant organism(MDRO)infection and its risk factors during the recovery period of patients with traumatic brain injury,and to construct and validate the predic-tion model for MDRO lung infection.METHODS Patients who were admitted to the Shanxi Second People's Hos-pital for traumatic brain injury(TBI)and developed hospital-acquired pneumonia(HAP)during their hospitaliza-tion between Aug.2019 and Aug.2023 were selected as the study subjects,their clinical data were retrospectively collected,and the patients were randomly divided into a modeling group(n=270)and a verification group(n=90)at a ratio of 3∶1,and they were further categorized into the MDRO infection group(148 cases)and the non-infection group(122 cases)based on whether they developed pneumonia caused by MDRO infections.Clinical data of the patients were collected for analysis,with pathogen identification performed for all patients.The risk factors for MDRO infection in TBI patients with HAP were analyzed by multivariate Logistic regression analysis,a pre-diction model for MDRO infections during the hospitalization and recovery period of TBI patients with HAP was constructed using a formula-based method,the goodness-of-fit of the model was assessed using Hosmer-Leme-show test,and its predictive ability was evaluated using receiver operating characteristic(ROC)curves.RESULTS There was no significant differences in the baseline characteristics of TBI patients with HAP between the modeling group and the verification group.The incidence of MDRO infections among TBI patients with HAP in modeling group was 54.81％(148/270),with Klebsiella pneumoniae(25.27％)being the predominant pathogen.Mechan-ical ventilation duration(OR=5.789,95％CI:2.164-15.486,P=0.001),ICU length of stay(OR=5.441,95％CI:2.153-13.751,P＜0.001),combined kidney diseases(OR=2.770,95％CI:1.321-5.812,P=0.007),duration of antibiotics use(OR=7.486,95％CI:1.928-29.059,P=0.004),occurrence of impaired consciousness(OR=5.720,95％CI:2.586-12.652,P＜0.001)and use of aminoglycoside antibiotics(OR=3.861,95％CI:1.608-9.273,P=0.003)were risk factors for MDRO infections.The Hosmer-Lemeshow for the risk prediction model showed that x2=5.013 and P=0.496.ROC curves analysis showed The area under the curve(AUC)for the modeling group by ROC curve analysis was 0.80(95％CI:0.773-0.877),with a sensitivi-ty of 83.33％and a specificity of 73.60％respectively,and the AUC for the verification group were 0.800(95％CI:0.670-0.895),with a sensitivity of 81.82％and a specificity of 77.27％respectively.CONCLUSION The prediction model constructed in this study,based on the risk factors for MDRO infection pneumonia during the re-covery period in hospitalized patients with traumatic brain injury,demonstrated high goodness-of-fit,as well as satisfactory sensitivity and specificity,and its application in clinical practice may help identify patients at high risk of MDRO infections.

Objective:To explore the prevalence and risk factors of insomnia in Chinese Air Force servicemen deployed to highland areas.Methods:A total of 718 Air Force servicemen deployed to Qinghai-Tibetan plateau were recruited at May 2024.Sleep quality was assessed with the Pittsburgh Sleep Quality Index.Social-demograph-ic,military service,and psychological characteristics were measured with a self-administered general question-naire.Bivariable and multivariable logistic regressions were performed to identify independent risk factors.Missing data were handled by the multiple imputation.Results:The average sleep duration was(6.9±1.2)h and the aver-age PSQI score was(5.9±4.1).Totally 53.8％of participants experienced clinically significant insomnia.The multivariable analysis revealed that age≥35(aOR=4.07,95％CI=1.11-17.76),stressful event(aOR=3.27,95％CI=2.00-5.49),dysfunctional sleep beliefs and attitudes(aOR=2.59,95％CI=1.75-3.85),and caffeine product usage(aOR=1.69,95％CI=1.17-2.43)were risk factors for insomnia,while Tibetan-indigenous ethnic(aOR=0.44,95％CI=0.20-0.91),higher perceived social support(aOR=0.96,95％CI=0.96-0.99),and positive coping style(aOR=0.96,95％CI=0.93-0.99)were protective factors.Conclusion:Air force service-men deployed to highland areas have sufficient sleep time,but reduced sleep quality.Age,exposed to stress event during deployment,dysfunctional sleep beliefs and attitudes,and caffeine product usage are risk factors for insomni-a,while Tibetan-indigenous ethnic,higher perceived social support and positive coping style act as protective fac-tors.

Objective To observe the value of cervical lymph node ultrasound network model based on deep learning(DL)for diagnosing cervical lymph node metastasis of papillary thyroid carcinoma(PTC).Methods Totally 444 PTC patients with suspected cervical lymph node enlargement on ultrasonography were retrospectively enrolled and divided into metastasis group(n=253)and non-metastasis group(n=191)based on fine needle aspiration pathology and thyroglobulin detection.And 1 754 cervical lymph node ultrasonic images were divided into training set(n=1 404),validation set(n=175)and test set(n=175)at the ratio of 8∶1∶1.Ultrasonic features of cervical lymph nodes were extracted,then ultrasound network model was established using DL,and the diagnostic efficacy of this model for diagnosing lymph node metastasis was analyzed.Results The sensitivity,specificity,accuracy and area under the curve of ultrasound network model for diagnosing cervical lymph node metastasis of PTC was 93.06％,97.05％,95.04％and 0.858 in validation set,which was 86.14％,78.67％,88.49％and 0.828 in test set,respectively.Conclusion Cervical lymph node ultrasound network model based on DL was helpful for diagnosing cervical lymph node metastasis of PTC.

Objective To compare the consistency of voice parameters collected by commonly used smart-phone brands in China and professional recording equipment,and to study whether smartphones can be used for voice research.Methods A total of 67 normal subjects were selected for voice recording using six different smart-phone brands(via the"Active Health"screening APP from the National Key Research and Development Program)and professional recording equipment.Acoustic voice parameters such as fundamental frequency parameters,fre-quency variation parameters,amplitude variation parameters,formant parameters,and energy parameters were ex-tracted from the vowels/a/,/i/,and/u/.A one-way ANOVA test and Tukey's HSD post-hoc comparisons were conducted on the independent variables.Results There were no significant differences between smartphones and professional recording equipment in terms of fundamental frequency parameters such as median F0,mean F0,max F0 and min F0;frequency parameters such as jitter local,jitter local absolute,jitter rap,jitter ppq5,and jitter ddp;amplitude parameters such as shimmer local,shimmer local dB,shimmer apq3,shimmer apq5,shimmer apq11,and shimmer dda;and formant parameters such as F1,F2,F3,and F4.However,significant differences were found in energy parameters such as mean energy(F=31.171,P＜0.001),max energy(F=34.193,P＜0.001),and min energy(F=5.453,P＜0.001)between smartphones and professional recording equipment.Conclusion The smartphones using the"Active Health"screening app from the National Key Research and Development Program can replace professional recording equipment for voice research.However,caution should be exercised when selec-ting energy-related acoustic parameters.

Objective:To explore the prevalence and risk factors of insomnia in Chinese Air Force servicemen deployed to highland areas.Methods:A total of 718 Air Force servicemen deployed to Qinghai-Tibetan plateau were recruited at May 2024.Sleep quality was assessed with the Pittsburgh Sleep Quality Index.Social-demograph-ic,military service,and psychological characteristics were measured with a self-administered general question-naire.Bivariable and multivariable logistic regressions were performed to identify independent risk factors.Missing data were handled by the multiple imputation.Results:The average sleep duration was(6.9±1.2)h and the aver-age PSQI score was(5.9±4.1).Totally 53.8％of participants experienced clinically significant insomnia.The multivariable analysis revealed that age≥35(aOR=4.07,95％CI=1.11-17.76),stressful event(aOR=3.27,95％CI=2.00-5.49),dysfunctional sleep beliefs and attitudes(aOR=2.59,95％CI=1.75-3.85),and caffeine product usage(aOR=1.69,95％CI=1.17-2.43)were risk factors for insomnia,while Tibetan-indigenous ethnic(aOR=0.44,95％CI=0.20-0.91),higher perceived social support(aOR=0.96,95％CI=0.96-0.99),and positive coping style(aOR=0.96,95％CI=0.93-0.99)were protective factors.Conclusion:Air force service-men deployed to highland areas have sufficient sleep time,but reduced sleep quality.Age,exposed to stress event during deployment,dysfunctional sleep beliefs and attitudes,and caffeine product usage are risk factors for insomni-a,while Tibetan-indigenous ethnic,higher perceived social support and positive coping style act as protective fac-tors.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

OBJECTIVE To investigate the inhibitory effect and mechanism of the active components of Alpinia katsumadai （ACAK） on tumor xenograft growth and tumor angiogenesis of human pancreatic cancer PANC-1 cells in nude mice. METHODS A tumor xenograft model in nude mice was established using human pancreatic cancer PANC-1 cells. The mice were randomly divided into model control group （intragastric administration of 0.9% normal saline）， solvent control group （intragastric administration of 0.5% carboxymethyl cellulose sodium）， positive control group （intraperitoneal injection of 0.5% carboxymethyl cellulose sodium+bevacizumab suspension 5 mg/kg ）， and ACAK 50， 100， and 200 mg/kg groups （intragastric administration of 0.5% carboxymethyl cellulose sodium+ACAK suspension 50， 100， 200 mg/kg）. The administration was carried out for 5 consecutive days followed by a 2-day interval， and this cycle was repeated for a total duration of 28 days. The tumor volume （TV）， relative tumor volume （RTV）， and relative tumor proliferation rate （T/C） at various time points from day 1 to day 28 after drug administration were measured and calculated for each group of nude mice. After the drug administration， the tumor weights were measured， and microvessel density （MVD） in the tumor xenograft tissues of nude mice， as well as relative protein expression levels of vascular endothelial growth factor （VEGF） and its receptor [fas-like tyrosine kinase-1 （Flt-1）， kinase insert domain receptor （KDR）] were detected. RESULTS On the 24th day of ACAK administration，compared with the model control group， the TV and RTV （except for ACAK 50 and 100 mg/kg groups） of nude mice in the positive control group and ACAK dose groups were significantly decreased （P＜0.05 or P＜0.01）， and the T/C of ACAK dose groups showed a dose-dependent decrease； the microvascular distribution of nude mice in the positive control group and ACAK dose groups was relatively sparse， and the tumor weight （except for the ACAK 50 mg/kg group）， MVD， and relative expression levels of VEGF， KDR， and Flt-1 in the tumor xenograft tissues were significantly reduced （P＜0.05 or P＜0.01）. CONCLUSIONS ACAK has a good anti-pancreatic cancer effect， and its mechanism may be related to its inhibition of VEGF/ VEGFR signaling pathway， thereby inhibiting angiogenesis in pancreatic cancer.